We consider a class of stochastic nonlinear complementarity
problems. We propose a new reformulation of the stochastic
complementarity problem, that is, a two-stage stochastic
mathematical programming model reformulation. Based on this
reformulation, we propose a smoothing-based sample average
approximation method for stochastic complementarity problem and
prove its convergence. As an application, a supply chain
super-network equilibrium is modeled as a stochastic nonlinear
complementarity problem and numerical results on the problem are
reported.